How Marketers use Machine Learning in Retail

Machine learning is revolutionising how companies are capitalising on Big Data to develop their marketing strategies. While the term encompasses a broad spectrum of technologies and approaches, in a marketing context it can be used to improve targeting, response rates and overall marketing ROI. To put it simply, machine learning involves the automated analysis of large volumes of data – such as consumer spending habits and purchasing behaviour, as well as demographic information – and using a mathematical algorithm and a computer to identify patterns and trends. The algorithm then tests predictions based on historical campaign data and learns from the predictions it gets right. With time, these algorithms become highly accurate as more data from campaign results is added.

When these trends and insights are used to develop a campaign or an entire marketing strategy, there’s considerably less guesswork and a greater chance of success. To get a better idea of machine learning in practice, let’s have a look at how two of the world’s top retailers are using machine learning to improve marketing ROI.

Machine learning is being used by marketers to identify patterns in purchase behaviour to develop relevant offers that are predicted to result in high response rates… sometimes a little too well. You may have heard about Target’s infamous machine learning incident where the company learned of a teenage girl’s pregnancy before her own family even knew. This is an interesting nugget in and of itself, but it’s important to understand the value of the machine learning methodology employed by Target to achieve such accuracy.

First of all, the company formed a strategic goal to target new parents as a customer segment. This demographic is perfect for a retailer, as they inevitably need a bunch of new products for the bundle of joy on his or her way.